SUMMARY ? OVERALL Cellular cryo-tomography has emerged as a critical tool for the visualization and structural study of the molecular nanomachines at the heart of cellular function. Although the basic electron cryo-tomography technique has been used for several decades, the technology is being revolutionized by recent advances in sample preparation, electron cryo-microscopy hardware, improved capabilities for automatic data collection, direct electron detection imaging devices, and phase plate technologies. Combined, these advances led to the ability to generate extraordinarily large numbers of cellular cryo-tomograms of exquisite quality. In principle, such large data sets offer insights into cellular variation in disease states as well as better insights into basic cellular function, opening new possibilities for studying the underpinnings of health and disease at the finest possible level, potentially leading to completely new diagnostics for cancer and other cell-altering diseases. However, collection of cellular data is now at a far faster rate than can currently be analyzed with existing methods, producing a serious barrier to progress: to match the data production rates of a single laboratory, at least 50 experienced scientists would need to handle the data analysis. The primary goal of this Program Project is to establish quantitative and highly automated tools for the reconstruction and interpretation of highly complex cellular tomographic data. We have assembled a highly synergistic team of PIs with complimentary expertise in cutting-edge computational and experimental electron microscopy techniques to achieve this goal through collaborative efforts. Project 1 (Hanein & Penczek) focuses on development and implementation of tomogram quality assessment and validation techniques and on experimentally guided optimization of data collection strategies. Project 2 focuses on automatic tomographic reconstruction technology, extraction of various features from the tomograms, and the analysis of distribution patterns derived from the extracted features. Project 3 focuses on development of quantitative tools for tomogram annotation through deep learning and sub-tomogram alignment as well as interactive visualization tools. The set of highly automated tools developed in this Program Project will permit us to interpret 5?10x as much data as is possible using existing methods, greatly expanding the types of cellular variations we can effectively study.